Join our team as a Custom Software Engineer and play a key role in developing innovative, AI-native, data-centric products. This position focuses on building intelligent data experiences like data agents and conversational analytics by leveraging SAP BTP Datasphere and advanced AI architectures.

We aim to move beyond traditional dashboards, creating solutions that offer grounded insights on governed Datasphere models. This involves combining strong enterprise data warehousing skills with cutting-edge AI technologies to deliver scalable and high-performing applications tailored to specific business needs.

Design and implement governed data products within SAP Datasphere, utilizing concepts like Spaces and shareable models. Build robust semantic models optimized for both analytics and AI consumption, ensuring clear definitions and lineage tracking.

Engineer retrieval strategies for AI applications, grounding responses in enterprise data from SAP and non-SAP sources. Develop data agents capable of planning, tool execution, context retrieval, and generating cited answers.

Facilitate hybrid modernization by supporting migration paths from legacy warehouse investments using patterns like SAP BW bridge. Implement layered architecture designs (e.g., Bronze/Silver/Gold) for data landing, cleansing, and publishing, embedding quality controls throughout the delivery lifecycle.

Establish evaluation loops for AI performance and add telemetry for AI interactions to enhance reliability and cost efficiency. Collaborate closely with business, data governance, and platform teams to align data products with strategic decisions and operational workflows.

A strong foundation in custom software engineering is essential, with a focus on developing and enhancing components across various systems. Expertise in SAP BTP Datasphere, including data modeling, spaces, sharing patterns, and enterprise semantic design, is required.

Proficiency in building with Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) techniques, covering retrieval, grounding, prompt/tool design, and evaluation, is crucial. Solid understanding of software engineering fundamentals, including testability, a CI/CD mindset, and reliable integrations, is also necessary.

Candidates with experience in migration/modernization using BW bridge patterns, layered architecture implementation, and familiarity with vector search/embedding pipelines will be strongly considered. This role requires a minimum of 3 years of relevant experience and completion of 15 years of full-time education.